Background: Demand for healthcare from hospitals in the United States is increasing due to an aging population with a high prevalence of chronic conditions, and the recent expansion of insurance coverage through the Affordable Care Act. Complicating the ability for hospitals to meet this demand is an increasingly difficult financial environment. Many policy makers and practitioners argue that the exchange of clinical data across the health care system is a key step in helping hospitals meet this demand due to its ability to improve hospital efficiency. However, existing research fails to evaluate the efficiency changes over time from hospital participation in health information exchanges (HIEs). This project addresses this gap by evaluating the efficiency changes over time from hospital participation in HIE. This proposal is in response to the special emphasis notice (NOT-HS-13-011) that calls for R36 grants focusing on health information technology, and specifically addresses the impact on outcomes area of research. Methodology: This research uses a national sample of all acute-care, non-federal hospitals in a market with at least one hospital actively participating in an HIE. Data sources include the 2009-2012 American Hospital Association (AHA) annual surveys and information technology (IT) supplements, and the 2009-2012 Center for Medicare and Medicaid Services (CMS) public data files. Efficiency change will be measured using the Malmquist algorithm, a type of Data Envelopment Analysis. The Malmquist yields three measures of efficiency change: total factor productivity (TFP), technological change (TC), and technical efficiency change (TEC). The geometric means of each of these three indices will be divided into quintiles and used as the dependent variable in ordinal logistic regression equations. Independent variables of interest will include whether the hospital has ever participated in HIE, cumulative years participation, and organizational affiliation of exchange partners. Selection bias will be corrected for using inverse probability weights. Endogeneity of HIE participation will be corrected for using propensity score adjustments. Preliminary analysis identified 1,303 hospitals that fit the inclusion criteria. Implications: The results of this study can assist policymakers as they attempt to evaluate the impact of the public investment in HIEs. Hospital administrators seeking to justify their private HIE investment decisions can also benefit from this study. The use of the three indices from the Malmquist algorithm also helps hospitals identify the specific mechanism that produce efficiency changes. Finally, the results of this study will be useful to researchers as they continue to evaluate the impact of new health information technologies.